An Introduction to Support Vector Machines and Other Kernel-based Learning Methods John Shawe-Taylor, Nello Cristianini
Publisher: Cambridge University Press

Such as statistical learning theory and Support Vector Machines,. PyML focuses on SVMs and other kernel methods. [1] An Introduction to Support Vector Machines and other kernel-based learning methods. Support Vector Machine (SVM) is a supervised learning algorithm developed by Vladimir Vapnik and his co-workers at AT&T Bell Labs in the mid 90's. Almost all of these machine learning processes are based on support vector machines or related algorithms, which at first glance seem unapproachably complex. Much better methods like logistic regression and support vector machines can be combined to give a hybrid machine learning approach. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Bpnn.py - Written by Neil Schemenauer, bpnn.py is used by an IBM article entitled "An introduction to neural networks". Shogun - The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) . Scale models using state-of-the-art machine learning methods for. It is supported on Linux Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. Publicus Groupe SA, issued in February 2012, giving a judicial imprimatur to use of “predictive coding” and other sophisticated iterative sampling techniques in satisfaction of discovery obligations, should assist in paving the way toward greater acceptance of these new methods. Nello Cristianini, John Shawer-Taylor [2] 数据挖掘中的新方法-支持向量机 邓乃扬， 田英杰 [3] 机器学习. John; An Introduction to Support Vector Machines and other kernel-based.